Download Belief Functions: Theory and Applications: Proceedings of by Hung T. Nguyen (auth.), Thierry Denoeux, Marie-Hélène Masson PDF
By Hung T. Nguyen (auth.), Thierry Denoeux, Marie-Hélène Masson (eds.)
The idea of trust features, sometimes called facts concept or Dempster-Shafer concept, used to be first brought by means of Arthur P. Dempster within the context of statistical inference, and used to be later constructed via Glenn Shafer as a normal framework for modeling epistemic uncertainty. those early contributions were the beginning issues of many vital advancements, together with the Transferable trust version and the speculation of tricks. the speculation of trust services is now good tested as a normal framework for reasoning with uncertainty, and has good understood connections to different frameworks comparable to chance, probability and obscure likelihood theories.
This quantity includes the court cases of the 2nd foreign convention on trust services that was once held in Compiègne, France on September 11 might 2012. It gathers fifty one contributions describing fresh advancements either on theoretical concerns (including approximation tools, mix principles, non-stop trust capabilities, graphical types and independence strategies) and functions in quite a few parts together with type, photograph processing, statistics and clever vehicles.
Read or Download Belief Functions: Theory and Applications: Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012 PDF
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Additional info for Belief Functions: Theory and Applications: Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012
13) The measure we presented can be therefore computed by pairwise comparison of the extremes as in (13) or by solving the above derived linear program. ). Thus, for worst case scenarios, linear programming is faster for large n, while pairwise comparison is faster for small values (the threshold being around n = 6). Some preliminary numerical tests on randomly generated BFs were performed to compare our interval-valued measure with other, singly-valued, descriptors. The results, summarized in Figure 3, suggests that our intervals are seemingly 44 A.
Imperfect information frequently occurs in video analytic processes. For example, a person may be classified as male with a certainty of 85% by a gender profiling algorithm. , we do not know how to distribute the remaining 15% between male and female. 15 (or interval probabilities), which is difficult to use for reasoning. Imperfect information is usually caused by ignorance or unreliability of the information sources. For example, a camera may have a faulty gain control setting, illumination could be poor, or the classifier training set may be unrepresentative.
Uncertain profiling results, unstable results over 36 J. Ma, W. Liu, and P. Miller time for a gender profiling classifier, and different classifiers capturing different features. We have shown that a DS-based approach handles these three issues in a seamless way. For future work, we plan to apply the fusion schemes to profiling classifier results generated from real video sequences. Acknowledgements. This research work is sponsored by the EPSRC projects EP/G034303/1 and EP/H049606/1 (the CSIT project).